Want a high-performing team? Add AI

By Jeffery Davis

75% of cross‑functional teams fail at core objectives, such as
staying on schedule and meeting customer expectations

New
AI tools can help lower the burden of administrative work, freeing
up managers to stay on task

Academic research suggests AI
tools will eventually become more like teammates that provide
emotional bonds for their human co‑workers

In late 2017, Bridgewater Associates founder Ray Dalio announced
that his company was developing a software “coach” that will guide
managers by crunching data on employees.

The coach is an AI‑powered update to an app that employees at the
world’s largest hedge fund already use to rate their colleagues 15
times a week across 75 attributes. Algorithms cull the data to create
pointillist feedback maps of skills, strengths and weaknesses.
Managers use the maps to build teams in which workers with the right
skills take on the right jobs.

“Knowing what people are like also allows us to decide what
responsibilities to give them and to weigh our decisions based on
people’s merits,” Dalio said in a TED Talk last year.

While many companies are focused on how AI and other
machine‑learning tools will work alongside people, there’s a problem
that has to be tackled first: People need to work alongside one
another in teams to achieve business goals. As it turns out, they’re
not always very good at it.

Three‑fourths of cross‑functional teams are dysfunctional, according
to a study by Stanford University management professor Behnam Tabrizi,
who found that most teams fail at three or more (out of five) core
responsibilities: planning a budget, staying on schedule, adhering to
specifications, meeting customer expectations, and keeping aligned
with company goals.

“Most companies aren’t ready for AI because they still haven’t
figured out how to work together as people,” says Dominic Price, the
R&D chief at Atlassian, a Sydney‑based company that builds
collaboration tools for businesses. “If we don’t fix the human
teamwork issues, imagine what happens when you add robots to the equation.”

Tabrizi’s findings point to three crucial areas for improving team
dynamics: Better management, clearer goals, and better team
communication. New AI‑enabled enterprise tools are bolstering progress
in each arenas. They hold promise for putting teams—and teamwork—back
on track.

Free the managers

Managers spend 54% of their time on
routine tasks like scheduling and managing emails, according to an Accenture report.
Just 10% of their time goes toward strategic planning, and even less
(7%) toward mentoring talent.

One of the major promises of AI is that it will free managers from
rote admin work, freeing them to lean into higher‑value tasks like
strategic planning and mentoring talent.

Today’s AI tools don’t just handle routine work. They can also take
on some of the load of managing a team. Stratejos, for instance, is a
tool with AI capabilities that acts as a virtual project manager,
tracking team performance through collaboration platforms like Jira,
Slack and Hipchat. It also lets managers track employee performance on
multiple tasks over time to enable targeted mentoring in skills that
may need improving.

“One of the core tasks managers measure success by is their ability
to predict,” says Avi Goldfarb, a professor of marketing at Rotman
University and co‑author of Prediction Machine: The Simple
Economics of Artificial Intelligence. He cites HR managers, who
predict who’s going to be a good employee, as an example. “With AI,
prediction will become a less important part of the job and coaching
is going to become more important.”

Some AI‑driven software even enables self‑management by team
members. Yva Pulse is a tool that provides team members with their own
performance metrics so they can identify areas of weakness they need
to address on their own initiative.

“As machines assume more of the prediction process, things that
require judgment and social skills will become more valuable in
management,” Goldfarb says.

Predictive data

To be successful, teams need to work towards clearly defined, shared
goals. AI can’t call the shots. But predictive AI tools can help
companies pinpoint where to focus.

“AI can help people agree on what a goal is by predicting
outcomes—but prediction itself isn’t decision‑making,” says Goldfarb.
“Goal‑setting is a human task.”

Where AI comes into the equation is in crunching data. Today,
there’s more data to crunch, and more insights to mine, than ever
before. Armed with predictive, data‑driven insights, teams can set
targeted goals that will capitalize on business opportunities in the
near future and beyond.

Walmart, for example, uses AI capabilities in SAP HANA, a popular
relational database management system, to mine data from transactions
at its 11,000‑plus stores in real time. The system churns through over
200 streams of internal and external data, taking minutes to crunch
data that once took weeks to process.

Which social media ads will resonate with key demographics? Which
new products will fly off shelves in which regions? A company’s
internal teams—from marketing and HR to sales and logistics—can use
these insights to make complex business decisions to meet (and
continually adjust) their goals.

Clear goals aren’t that useful if team members aren’t on the same
page about what they’re trying to achieve. Goldfarb predicts that AI
will soon help detect subtle variations in individual perceptions of
shared goals.

“Let’s say two people are working on the same idea,” he says. “A
machine can detect whether they’re working towards the same objective.
You could do this through natural language processing or some
as‑yet‑invented tool for determining whether two different paragraphs
by two different writers are conveying the same idea.”

Nascent AI technology is already moving in this direction with smart
speech‑recognition tools like Gridspace. Practically speaking, it’s a
virtual note‑taker for business meetings. But beyond taking notes and
sharing them with stakeholders, Gridspace detects which parts of a
discussion are important based on who says what (it recognizes vocal
“fingerprints”), context and even inflection. It then highlights all
these points in an email rundown.

Participants can edit Gridspace notes to flag key priorities and
share them with colleagues. This feedback loop also helps Gridspace
learn how to better recognize company priorities in future meetings.

Keep it clear

In the end, a team lives or dies by its ability to communicate. But
even if everyone on your team is yakking on Slack, Hipchat or Jira,
they aren’t necessarily communicating effectively.

Several new applications based on
machine‑learning algorithms are geared toward ensuring that they are.
Virtual assistant Talla, chatbot Howdy and voice assistant MindMeld
can all be integrated into Slack and other platforms to let teams tap
a company’s internal data and share knowledge in real time.

Even with these artificially intelligent bells and whistles, modern
collaboration platforms can actually hinder effective team
communication. As Basecamp founder and CEO Jason Fried says, “group
chat is like being in an all‑day meeting with random participants and
no agenda.”

The mass adoption of new digital communication tools may, in fact,
be contributing more to team failure than success. Price recently
asked Atlassian’s engineers what makes teams great.

“We got this Tourette’s‑like response where they all just started
shouting, ‘Tools! Tools, tools, and more tools,’” he says. “You have
to take a step back from being highly reliant on tools to deliver
great outcomes and ask, how can we empower these people to alter the
way they work.”

Bridgewater is not alone in its expectation that AI will solve that
riddle for teams and organizations before they ever figure it out
themselves. Some emerging AI applications are geared toward
strengthening emotional bonds between team members. Crystal, for one,
is an AI algorithm that can identify basic personality traits of
colleagues or contacts by sorting through public social media data. It
then offers targeted advice on how best to communicate with them.

Bonding with AI

After thousands of years, humans still haven’t mastered the art of
collaboration. What new challenges can we expect when AI transitions
from work tool to full‑fledged work partner?

It’s not a hypothetical question. By 2022, one in five employees
will have AI as a co‑worker in the form of a chatbot, a voice‑ and
vision‑enabled AI assistant or even a robot, according to a Gartner study.

Some experts say this shift will impact team dynamics favorably,
even predicting that AI will provide emotional benefits to human workers.

“We have an opportunity to create new kinds of interactions,” says
Guy Hoffman, a Cornell researcher who designs AI systems and robots.
“Any time we have new tech, we get new relationships.”

Hoffman conducted a study in which humans worked with robot
teammates who were programmed to behave like humans. As expected, he
found that human/machine collaboration increased productivity. He was
surprised, however, to learn that his human subjects formed an
emotional bond with the machines.

At the end of the study, the human participants reported feeling
“tenderness,” “amusement,” “respect,” and “trust” for their new
teammates. They also used human pronouns to describe their robotic co‑workers.

On its face, the takeaway is ironic but also optimistic. It may be
that artificial intelligence is the missing ingredient that can help
human teams succeed.

Jeffrey Davis, a founding editor of Business 2.0 magazine and
former executive editor at CBS Interactive, writes frequently about
technology and business.